1. Technical Field of the Invention
The invention relates generally to communication systems; and, more particularly, it relates to performing detecting and/or calculating soft information that is employed when performing iterative decoding processing of coded signals of such communication systems.
2. Description of Related Art
Data communication systems have been under continual development for many years. One such type of communication system that continues to be of significant interest is that which employs iterative error correction codes. Some examples of iterative correction codes include LDPC (Low Density Parity Check) codes and turbo codes. Communications systems with iterative codes are often able to achieve lower BER (Bit Error Rate) than alternative codes for a given SNR (Signal to Noise Ratio).
A continual and primary directive in this area of development has been to try continually to lower the SNR required to achieve a given BER within a communication system. The ideal goal has been to try to reach Shannon's limit in a communication channel. Shannon's limit may be viewed as being the data rate to be used in a communication channel, having a particular SNR, that achieves error free transmission through the communication channel. In other words, the Shannon limit is the theoretical bound for channel capacity for a given modulation and code rate.
Looking at error correcting LDPC codes, various types of LDPC codes have been shown to provide for excellent decoding performance that can approach the Shannon limit in some cases. For example, some LDPC decoders have been shown to come within 0.3 dB (decibels) from the theoretical Shannon limit. While this example was achieved using an irregular LDPC code of a length of one million, it nevertheless demonstrates the very promising application of LDPC codes within communication systems.
Error correcting codes can be employed within any communication system in which correction of errors is desired. Many iterative decoders that decode according to an error correcting code perform detection which involves calculating soft information which is used for the iterative decoding processing. One prior art approach for performing this detection is the BCJR approach as described in the following reference [a].    [a] L. R. Bahl, J. Cocke, F. Jelinek and J. Raviv, “Optimal decoding of linear codes for minimizing symbol error rate,” IEEE Trans. Inform. Theory, vol. 20, pp. 284-287, March 1974.
The approach as described in reference [a] approach is sometimes also referred to as the MAP detection approach, which maximizes the “a posteriori” probability thereby minimizing the error probability. For example, in the problem of detection in the presence of noise, it is well-known that the estimate which maximizes the “a posteriori” probability minimizes the error probability. If it is desired to perform sequence detection in the presence of additive noise, then if the value r denotes the received sequence, then the sequence t which maximizes the probability, p(t|r), is called the MAP estimate. This MAP estimate minimizes the probability of sequence error. If the symbol-by-symbol MAP estimates are desired, then the estimate which maximizes the “a posteriori” symbol probability minimizes the symbol error probability.
The BCJR approach, as described in reference [a], can be used to find the symbol-by-symbol MAP estimates in some communication system applications, and it is widely used in turbo codes since it can be used to compute the soft information (e.g., the LLRs (log likelihood ratios)). However, there are many instances in which the BCJR approach is not sufficient or adequate to perform the detection. For example, when the additive noise is uncorrelated, then the BCJR approach works well. However, some applications include noise which is not additive white, but the noise is additive colored. The BCJR approach does not work well in such applications. For example, in magnetic recording systems, the communication channel is not an AWGN (Additive White Gaussian Noise) communication channel. In addition, the noise in some communication systems is also signal-dependent which makes the straightforward BCJR approach described in reference [a] infeasible.
In general, signals that have been corrupted by colored and/or signal-dependent noise cannot rely on the BCJR approach for performing detection. Nevertheless, within communication systems that employ iterative error correction decoding processing, the soft information (e.g., the LLRs) still needs to be calculated. Unfortunately, the BCJR approach to calculating this soft information cannot be employed for communication systems including signals that are corrupted by colored and/or signal-dependent noise.
There does not presently exist in the art a means to do this within communication systems employing iterative error correction decoding processing that have signals that are corrupted by colored, signal-dependent noise. Some examples of such communication systems include magnetic recording systems and other communication system types. There exists a need in the art to calculate such soft information for use in iterative decoding processing when the signals are in fact corrupted by colored and/or signal-dependent noise.